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--- |
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license: other |
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license_name: yi-license |
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license_link: https://huggingface.co/01-ai/Yi-34B-200K/blob/main/LICENSE |
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datasets: |
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- ai2_arc |
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- unalignment/spicy-3.1 |
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- codeparrot/apps |
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- facebook/belebele |
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- boolq |
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- jondurbin/cinematika-v0.1 |
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- drop |
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- lmsys/lmsys-chat-1m |
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- TIGER-Lab/MathInstruct |
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- cais/mmlu |
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- Muennighoff/natural-instructions |
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- openbookqa |
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- piqa |
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- Vezora/Tested-22k-Python-Alpaca |
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- cakiki/rosetta-code |
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- Open-Orca/SlimOrca |
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- spider |
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- squad_v2 |
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- migtissera/Synthia-v1.3 |
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- datasets/winogrande |
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- nvidia/HelpSteer |
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- Intel/orca_dpo_pairs |
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- unalignment/toxic-dpo-v0.1 |
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- jondurbin/truthy-dpo-v0.1 |
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- allenai/ultrafeedback_binarized_cleaned |
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- Squish42/bluemoon-fandom-1-1-rp-cleaned |
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- LDJnr/Capybara |
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- JULIELab/EmoBank |
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- kingbri/PIPPA-shareGPT |
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--- |
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# A bagel, with everything (except DPO) |
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## Overview |
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An experimental fine-tune of yi-34b-200k using [bagel](https://github.com/jondurbin/bagel) |
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This is the model after the SFT phase, before DPO has been applied. You'll likely want to use the DPO'd version, rather than this one, but since I had it, I uploaded it. |
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### Data sources |
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*Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check* |
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- [ai2_arc](https://huggingface.co/datasets/ai2_arc) |
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- Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent. |
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- [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1) |
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- Variety of categories of synthetic instructions generated by gpt-4. |
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- [apps](https://huggingface.co/datasets/codeparrot/apps) |
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- Python coding dataset with 10k problems. |
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- [belebele](https://huggingface.co/datasets/facebook/belebele) |
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- Multi-lingual reading comprehension dataset. |
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- [bluemoon](https://huggingface.co/datasets/Squish42/bluemoon-fandom-1-1-rp-cleaned) |
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- Roleplay data scraped from Bluemoon, then cleaned and formatted as ShareGPT. |
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- [boolq](https://huggingface.co/datasets/boolq) |
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- Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?) |
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- [capybara](https://huggingface.co/datasets/LDJnr/Capybara) |
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- Multi-turn dataset used to create the capybara models. |
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- [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text) |
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- RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be. |
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- [drop](https://huggingface.co/datasets/drop) |
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- More reading comprehension. |
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- [emobank](https://github.com/JULIELab/EmoBank) |
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- Emotion annotations using the Valence-Arousal-Domninance scheme. |
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- [gutenberg](https://www.gutenberg.org/) (plain text) |
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- Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize) |
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- [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO) |
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- Chats collected by the lmsys chat arena, containing a wide variety of chats with various models. |
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- [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct) |
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- Composite dataset with a variety of math-related tasks and problem/question formats. |
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- [mmlu](https://huggingface.co/datasets/cais/mmlu) |
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- Massive Multitask Language Understanding - a wide variety of questions about various subject matters. |
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- [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions) |
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- Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type) |
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- [openbookqa](https://huggingface.co/datasets/openbookqa) |
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- Question answering dataset. |
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- [pippa](https://huggingface.co/datasets/kingbri/PIPPA-shareGPT) |
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- Deduped version of [PIPPA](https://huggingface.co/datasets/PygmalionAI/PIPPA) in ShareGPT format. |
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- [piqa](https://huggingface.co/datasets/piqa) |
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- Phyiscal interaction question answering. |
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- [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca) |
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- Python instruction response pairs, validated as functional. |
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- [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code) |
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- Code problems and solutions in a variety of programming languages taken from rosettacode.org. |
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- [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca) |
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- Collection of ~500k gpt-4 verified chats from OpenOrca. |
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- [spider](https://huggingface.co/datasets/spider) |
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- SQL-targeted dataset. |
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- [squad_v2](https://huggingface.co/datasets/squad_v2) |
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- Contextual question answering (RAG). |
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- [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3) |
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- GPT-4 generated data using advanced prompting from Migel Tissera. |
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- [winogrande](https://huggingface.co/datasets/winogrande) |
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- Fill in the blank style prompts. |
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Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss). |
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## Prompt formatting |
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In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta). |
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I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format. |
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This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate. |
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### Alpaca (sort of) |
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``` |
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Below is an instruction that describes a task. Write a response that appropriately completes the request. |
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### Instruction: |
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{system prompt, if provided} |
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{instruction} |
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### Response: |
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``` |
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The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section. |
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### Vicuna |
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``` |
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{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."} |
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USER: {instruction} |
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ASSISTANT: |
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``` |
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### ChatML (sort of) |
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I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong). |
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So, instead of: |
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```text |
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{bos}<|im_start|>{role} |
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{text} |
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<|im_end|>{eos} |
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``` |
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I just changed it to: |
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```text |
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{bos}{role} |
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{text} |
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{eos} |
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``` |
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If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `<s>` and `<|im_end|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune. |
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### Llama-2 chat |
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``` |
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[INST] <<SYS>> |
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{system} |
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<</SYS>> |
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{instruction} [/INST] |
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``` |
